{
 "metadata": {
  "name": "WAFO Chapter 5"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chapter 5 Extreme value analysis\n",
      "=================================\n",
      "Section 5.1 Weibull and Gumbel papers\n",
      "--------------------------------------\n",
      "Significant wave-height data on Weibull paper,\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "import wafo.data as wd\n",
      "import wafo.stats as ws\n",
      "import matplotlib.pyplot as plt\n",
      "Hs = wd.atlantic()\n",
      "wei = ws.weibull_min.fit2(Hs)\n",
      "tmp = ws.probplot(Hs, wei.par, dist='weibull_min', plot=plt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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LMt+8eZOff/6Z5cuXk5yczKBBgx64QCGqE/UmKnHgbAtP74csG/i6H6Q5AvHA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\nSUVORK5CYII=\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Significant wave-height data on Gumbel paper,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gum = ws.gumbel_r.fit2(Hs)\n",
      "tmp = ws.probplot(Hs, gum.par, dist='gumbel_r', plot=plt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\estimation.py:722: RuntimeWarning: invalid value encountered in sqrt\n",
        "  self.par_lower = self.par - zcrit * sqrt(pvar)\n",
        "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\estimation.py:723: RuntimeWarning: invalid value encountered in sqrt\n",
        "  self.par_upper = self.par + zcrit * sqrt(pvar)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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QEN3XAQEB8uhHoXdhYRHs3XsSdXTfHdgKrAGGg/k0eCICfCPxT3masJfXG7VW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\niZHgF0IIEyPBL4QQJkaCXwghTMz/A2nM+6pk09GHAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Significant wave-height data on Normal probability  paper,\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tmp = ws.probplot(np.log(Hs), plot=plt)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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B3MAjSx3tn3aCTV5QYIJ6UTcISAUuod5hS3r7wjDuqOd/J8E/adIkXF1d8ff3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\niqIo06dPV6ZPn27giqp37Ngx5fjx40p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      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Return values in the Gumbel distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clf()\n",
      "T=np.r_[1:100001]\n",
      "#sT=gum.par[1] - gum.par[0]*log(-log(1-1./T));\n",
      "sT = gum.isf(1./T)\n",
      "semilogx(T,sT), hold\n",
      "N=np.r_[1:len(Hs)+1]; \n",
      "Nmax=max(N);\n",
      "plot(Nmax/N,sort(Hs)[::-1],'.')\n",
      "title('Return values in the Gumbel model')\n",
      "xlabel('Return period')\n",
      "ylabel('Return value') \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x6d23570>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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rDqA9N/lCNWherSU/9JtE+bKujk6keNAQE1IsJSVZnb5vvw116liTwNwYmcCj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\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Section 5.2 Generalized Pareto and Extreme Value distributions\n",
      "----------------------------------------------------------\n",
      "Section 5.2.1 Generalized Extreme Value distribution\n",
      "-------------------------------------------------\n",
      "\n",
      "Empirical distribution of significant wave-height with estimated Generalized Extreme Value distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gev = ws.genextreme.fit2(Hs)\n",
      "gev.plotesf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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GYaxcaRiGYZw9axgREebPnj0Nw+wNNx8REU6OV/Ip6ndngaWnc+fOUbly5XzP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\nAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.kdetools as wk\n",
      "wk.TKDE(Hs, L2=0.5)(output='plot').plot('g--')\n",
      "plt.hold(True)\n",
      "gev.plotepdf() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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J+++/n8GDB1fl96y2zu3ZSl5gO71j2N2A1gMoUkXsdDvNLV26wLp1cM89escS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\nlCooKHCIN4vrJSUlqTZt2piLw7333qu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      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Analysis of yura87 wave data. \n",
      " Wave data interpolated (spline) and organized in 5-minute intervals\n",
      "Normalized to mean 0 and std = 1 to get stationary conditions. \n",
      "maximum level over each 5-minute interval analysed by GEV"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.interpolate as si\n",
      "xn  = wd.yura87()\n",
      "XI  = np.r_[0:len(xn):0.25]\n",
      "N   = len(XI); \n",
      "N = N-np.mod(N,4*60*5); \n",
      "YI  = si.UnivariateSpline(xn[:,0].ravel(),xn[:,1].ravel(),k=3,s=0)(XI[:N])\n",
      "YI  = np.reshape(YI, (4*60*5, N/(4*60*5))); # Each column holds 5 minutes of interpolated data.\n",
      "Y5  = (YI-YI.mean(axis=0))/(YI.std(axis=0))\n",
      "Y5M = Y5.max(axis=0)\n",
      "Y5gev = ws.genextreme.fit2(Y5M,method='mps')\n",
      "Y5gev.plotesf()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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fubO0sOvig2Du6f3uu+9y2223kZ2dzZtvvsmsWbNITk4u9E2Lizq9RVxHePhV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\nNlHCEBERmyhhiIiITZQwRETEJv8HnYF4liaW+BgAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Section 5.2.2 Generalized Pareto distribution\n",
      "-------------------------------------------\n",
      "Exceedances of significant wave-height data over level 3."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gpd3 = ws.genpareto.fit2(Hs[Hs>3],floc=3)\n",
      "gpd3.plotesf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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w4ME8ePCgOGLLF9lHIUQRtWmjVrO/+grGj4f0dH0H2if3XIwZo22YouiMto9i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\nQgghRK7+P5JGatzzLvuCAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Exceedances of significant wave-height data over level 7,"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gpd7 = ws.genpareto.fit2(Hs[Hs>7],floc=7)\n",
      "gpd7.plotesf()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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iZJsorl27xuDBg3FwcMDBwYFBgwZx/fr1wogtR7TDnUgp5+Bg1iz27jWbEz16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\nSUVORK5CYII=\n"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Simulates 100 values from the GEV distribution with parameters (0.3, 1, 2), then estimates the\n",
      "parameters using two different methods and plots the estimated distribution functions together\n",
      "with the empirical distribution.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Rgev = ws.genextreme.rvs(0.3,1,2,size=100)\n",
      "gp = ws.genextreme.fit2(Rgev,method='mps');\n",
      "gm = ws.genextreme.fit2(Rgev,method='ml');\n",
      "\n",
      "gp.plotesf()\n",
      "plt.hold(True)\n",
      "gm.plotesf('r--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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up1lRTlwzObz00kt4e3uzY8cOvL29rbeaNWvSr1+/soxRRMTugoLM4ntBPy6G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\n"
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Similarly for the GPD distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Rgpd = ws.genpareto.rvs(0.4,size=100);\n",
      "gmps = ws.genpareto.fit2(Rgpd, method='mps')\n",
      "gml = ws.genpareto.fit2(Rgpd, method='ml')\n",
      "gmps.plotesf()\n",
      "plt.hold(True)\n",
      "gml.plotesf('r--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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PVY2OjsbOnTtRWFiILl264I033kBMTAySk5Mxd+5cVFZWYubMmXjllVca/N6NWVjyRw4O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\nQmCC\n"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Return values for the GEV distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "T = logspace(1,5,10);\n",
      "sT = Y5gev.isf(1./T);\n",
      "\n",
      "\n",
      "clf\n",
      "semilogx(T,sT,T,sTlo,'r',T,sTup,'r'), hold\n",
      "N=1:length(Y5M); Nmax=max(N);\n",
      "plot(Nmax./N,sort(Y5M,'descend'),'.')\n",
      "title('Return values in the GEV model')\n",
      "xlabel('Return priod')\n",
      "ylabel('Return value') \n",
      "grid on "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import wafo.stats as ws\n",
      "R = ws.genpareto.rvs(-0.5,size=100);\n",
      "phat = ws.genpareto.fit2(R[R>.5], -.5, scale=1, floc=0.5)\n",
      "phat.plotfitsummary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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jR+YsXzp4eXnh5OQE/GOn27ZtA1DLYQwbNozt27dTq1atAgP3xo0bx86dOzEy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      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Better CI for phat.par[i=0] shape parameter\n",
      "Lp0 = phat.profile(i=0, pmin=-1,pmax=1)\n",
      "Lp0.plot()\n",
      "phat0_ci = Lp0.get_bounds(alpha=0.1)\n",
      "print 'phat0_ci = ', phat0_ci\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "phat0_ci =  [-0.73845586 -0.30183734]\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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bNmzYQKVKlQgJCSnyE5dE48bBG29IURHCXvn7Q//+euKkKLw8C8utg/UZGRmA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\nixBCCKuSwiKEEMKqpLAIIYS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      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Better CI for phat.par[i=2] scale \n",
      "Lp2 = phat.profile(i=2,pmin=0.1,pmax=2)\n",
      "Lp2.plot()\n",
      "phat2_ci = Lp2.get_bounds(alpha=0.1)\n",
      "print 'phat2_ci = ', phat2_ci\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "phat2_ci =  [ 0.55127823  0.97075832]\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Ss7ikeGIduY6R7Nixg5kzZ1KqVCnGjRuHv7+/tWMrMKPGSCZOhD//hNmzrf7S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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SF = 1./990\n",
      "x = phat.isf(SF)\n",
      "\n",
      "# CI for x\n",
      "Lx = phat.profile(i=2, x=x, link=phat.dist.link)\n",
      "Lx.plot()\n",
      "x_ci = Lx.get_bounds(alpha=0.2)\n",
      "print 'X_c = ', x_ci"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "X_c =  [ 1.78350616  2.09079614]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "c:\\pab\\workspace\\pywafo_svn\\pywafo\\src\\wafo\\stats\\distributions.py:4011: RuntimeWarning: invalid value encountered in true_divide\n",
        "  return where((c != 0) & (-inf < log_sf), expm1(-c * log_sf) / c, -log_sf)\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    }
   ],
   "metadata": {}
  }
 ]
}